US2025258247A1PendingUtilityA1
Dynamic time warping (dtw)-based real-time diagnosis method for commutation failure (cf) of phase-controlled converter
Assignee: HEFEI INST OF PHYSICAL SCIENCE CASPriority: Aug 22, 2024Filed: Apr 3, 2025Published: Aug 14, 2025
Est. expiryAug 22, 2044(~18.1 yrs left)· nominal 20-yr term from priority
Inventors:Xiaojiao ChenZhenyu XueXiaoxiao YangLiansheng HuangYan-Ting ShenShiying HeXiuqing ZhangYing Zuo
H02M 1/32H02M 7/219H02M 7/162H02M 1/0012G01R 31/42G01R 31/40H02M 7/155
64
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Claims
Abstract
Provided is a dynamic time warping (DTW)-based real-time diagnosis method for a commutation failure (CF). The method includes: designing a time series template S0 containing a change of a firing angle of a phase-controlled rectifier, and having a length n and a Euclidean distance L; using an n*n matrix M to represent a Euclidean distance between each point in the S0 and each point in the Si, and constricting the matrix M with an Itakura window; calculating values of the matrix M in the search range H; setting different weights for data at different locations, and searching a shortest path L* from point (x1, y1) to point (xn, yn) of the matrix; and setting a range of the distance L of the S0 as λ, determining that the CF fault occurs if the L* is greater than λL, or otherwise, determining that a system works normally.
Claims
exact text as granted — not AI-modifiedWhat is claimed is:
1 . A dynamic time warping (DTW)-based real-time diagnosis method for a commutation failure (CF) of a phase-controlled converter, comprising the following steps:
step 1: designing a voltage waveform with a time series of a length n as a time series template S0, S0=(y 1 −y n ), and calculating a Euclidean distance L of the time series template S0, y 1 −y n being n pieces of data in the time series template S0; step 2: taking data of n sampling points in a present acquired time series as an input sample Si, Si=(x 1 −x n ), aligning a start point and an end point of the time series template S0 with a start point and an end point of the input sample Si, and checking monotonicity of the time series template S0 and monotonicity of the input sample Si to ensure no crossed correspondence, x 1 −x n being data of n sampling points in the input sample Si, and i representing an ith input sample; step 3: establishing a coordinate system with the data of the n sampling points in the input sample Si=(x 1 −x n ) as an abscissa, and the n pieces of data in the time series template S0=(y 1 −y n ) as an ordinate, an n*n matrix M representing a Euclidean distance between each point in the time series template S0 and each point in the input sample Si, and constricting the n*n matrix M with an Itakura window to determine a search range H; step 4: calculating only values of matrix elements of the n*n matrix M in the search range H; step 5: in the search range H, setting different weights for the values of the matrix elements in the n*n matrix M, and searching a shortest path L* on which a sum of values of matrix elements in the n*n matrix M from a coordinate point (x 1 , y 1 ) to a coordinate point (x n , y n ) is minimum; step 6: setting a range of the Euclidean distance L of the time series template S0 as λ; and step 7: if the shortest path L* is greater than λL, determining that the CF fault occurs, or otherwise, determining that a system works normally.
2 . The DTW-based real-time diagnosis method for a CF of a phase-controlled converter according to claim 1 , wherein the step 1 comprises:
step 1.1: setting a range of a firing angle: since the CF occurs in an inverting state of the phase-controlled converter, setting the range of the firing angle as π/2−5π/6; step 1.2: calculating a distance L′ between a working voltage corresponding to a firing angle α 1 and a working voltage corresponding to a firing angle α 2 according to a Euclidean distance formula:
L
′
=
U
∑
(
6
sin
(
wt
+
α
1
+
x
)
-
6
sin
(
wt
+
α
2
+
x
)
)
2
,
wherein, x is a phase angle of a sampling point, U is an input voltage, w is an angular frequency, and t is time;
step 1.3: obtaining a universal Euclidean distance L u :
L
u
=
U
∑
(
6
sin
(
wt
+
α
1
+
x
)
-
U
d
(
α
1
)
-
6
sin
(
wt
+
α
2
+
x
)
+
U
d
(
α
2
)
)
2
,
wherein, an average U d for line voltages is:
U d =2.34U cos α,
wherein, α is the firing angle;
step 1.4: setting the universal Euclidean distance L u and the input voltage U as a positively related functional relationship, specifically:
L u =kU,
wherein, a coefficient k is:
k
=
∑
(
6
sin
(
wt
+
α
1
+
x
)
-
U
d
(
α
1
)
-
6
sin
(
wt
+
α
2
+
x
)
+
U
d
(
α
2
)
)
2
,
step 1.5: setting the firing angle α 1 as π/2 and the firing angle α 2 as π/2, sequentially increasing the firing angle α 2 by π/36 until the firing angle α 2 is 5π/6, and calculating and drawing a first curve; then setting the firing angle α 1 as π/2+π/36 and the firing angle α 2 as π/2, sequentially increasing the firing angle α 2 by π/36 until the firing angle α 2 is 5π/6, and calculating and drawing a second curve; and by the same reasoning, calculating and drawing 13 curves, and displaying a change of the coefficient k at each firing angle, thereby obtaining a coefficient map;
step 1.6: according to the coefficient map, obtaining a ninth curve with a corresponding Euclidean distance being not greater than 6 U at maximum, specifically setting a time series of a voltage waveform at the firing angle α 1 of 130° as the optimal time series template; and
step 1.7: according to a range of the corresponding Euclidean distance L* u : 0≤L* u ≤6U, determining the Euclidean distance of the optimal time series template as 6 U.
3 . The DTW-based real-time diagnosis method for a CF of a phase-controlled converter according to claim 1 , wherein the step 5 comprises: setting weights corresponding to front n/3 matrix elements as q1, weights corresponding to middle n/3−5n/6 matrix elements as q2, and weights corresponding to rear 5n/6−n matrix elements as q3.
4 . The DTW-based real-time diagnosis method for a CF of a phase-controlled converter according to claim 1 , wherein in the step 3, an element M mj in the n*n matrix M is calculated by:
M
mj
=
❘
"\[LeftBracketingBar]"
x
m
-
y
m
❘
"\[RightBracketingBar]"
2
,
m
=
1
,
2
…
n
;
j
=
1
,
2
…
n
,
wherein, x m represents data of an mth sampling point in the input sample Si, y m represents mth data in the time series template S0, and j is an index value.
5 . The DTW-based real-time diagnosis method for a CF of a phase-controlled converter according to claim 1 , wherein in the step 3, the search range H is constricted in a parallelogram with slopes being ½ and 2 respectively, a path is planned from the coordinate point (x 1 , y 1 ) to the coordinate point (x n , y n ), and four coordinate points of the parallelogram of the search range H are respectively (x 1 , y 1 ), (x n/3 , y 2n/3 ), (x 2n/3 , y n/3 ), and (x n , y n ).
6 . The DTW-based real-time diagnosis method for a CF of a phase-controlled converter according to claim 1 , wherein in the step 6, the λ is 1.5.
7 . The DTW-based real-time diagnosis method for a CF of a phase-controlled converter according to claim 3 , wherein the q1 is 0.5, the q2 is 0.8, and the q3 is 1.3.Join the waitlist — get patent alerts
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